1. Field of the Invention
The present invention relates to a defect inspection method of inspecting whether an object of inspection has a defect or not and a defect inspection apparatus used for the inspection.
2. Related Art
When the surface of an object such as a semiconductor wafer is to be inspected for a defect, an optimum inspection condition differs depending upon the type of the object of inspection (a workpiece) and a manufacturing process. Heretofore, to achieve such an optimum inspection condition an inspector is required to be individually checked and set for every type of object of inspection and every manufacturing process.
However, in case the types of objects of inspection are very many and new types are successively required to be inspected for a short period, it is cumbersome and inefficient to individually check and further set an optimum inspection condition. Further, even if the automation of such inspection is tried, it is difficult to automate the setting of an inspection condition.
The invention is made in view of the problems of the related type technique and has a technical object of providing a defect inspection method and a defect inspection apparatus using the method wherein a new type of object of inspection can be efficiently inspected and the automation of inspection is enabled.
To achieve the above object, according to a first aspect of the invention, there is provided a defect inspection method of inspecting a presence of a defect on an object, the defect inspection method including the steps of:
inputting a wavelength characteristic of each of a plurality or samples with wavelength variation of an illumination light for inspection;
inputting an inspection condition which an inspector voluntary sets for each sample as a teaching signal;
learning and storing a relationship between the inputted wavelength characteristic of each sample and the inputted inspection condition for each sample as a learning;
inputting a wavelength characteristic of the object with the wavelength variation of the illumination light;
determining an inspection condition for the object based on the inputted wavelength characteristic of the object and the learning; and
detecting a defect of the object based on the determined inspection condition for the object.
According to a second aspect of the invention, the defect inspection method of the first aspect further includes the step of:
learning and storing a relationship between the inputted wavelength characteristic of the object and the determined inspection condition for the object as the learning.
According to a third aspect of the invention, the defect inspection method of the first aspect, in the learning and storing step, the relationship between the wavelength characteristic of the following sample and the inspection condition for the following sample is learned and stored referring to the relationship between the wavelength characteristic of the previous sample and the inspection condition for the previous sample.
According to a fourth aspect of the invention, the defect inspection method of the first aspect, the leaning and storing step is executed by a neural network.
According to a fifth aspect of the invention, the defect inspection method of the first aspect, the defect inspection method includes a method of macroscopically inspecting the presence of the defect on the object having a minute pattern.
According to a sixth aspect of the invention, the defect inspection method of the first aspect further includes the steps of:
illuminating each of the samples and the object while varying a wavelength of the illumination light;
capturing images of each of the samples and the object using reflected lights or transmitted lights of the illumination lights having different wavelengths; and
detecting the wavelength characteristic of each of the samples and the object based on the images captured using the illumination lights having different wavelengths.
According to a seventh aspect of the invention, the defect inspection method of the sixth aspect, the wavelength characteristic includes luminance information of the reflected lights or the transmitted lights with the wavelength variation of the illumination light.
According to an eighth aspect of the invention, the defect inspection method of the first aspect, the detecting step includes the steps of:
executing differential processing between a reference image data and an image of the object for obtaining a differential image;
binarizing the differential image for obtaining a binarization data; and
detecting a defect based on the binarization data.
According to a ninth aspect of the invention, the defect inspection method of the eighth aspect, the inspection condition includes a threshold level referred in the binarizing step.
According to a tenth aspect of the invention, the defect inspection method of the eighth aspect, the detecting step includes a step of filtering the differential image, the inspection condition includes a parameter for the filtering.
Further to achieve the object, according to an eleventh aspect of the invention, there is provided a defect inspection apparatus for inspecting a presence of a defect on an object, the defect inspection apparatus including:
a first input unit which inputs a wavelength characteristic of each of a plurality of samples with wavelength variation of an illumination light for inspection;
a second input unit which inputs an inspection condition which an inspector voluntary sets for each sample as a teaching signal;
a third input unit which inputs a wavelength characteristic of the object with the wavelength variation of the illumination light;
a learning unit which learns and stores a relationship between the inputted wavelength characteristic of each sample and the inputted inspection condition for each sample as a learning, and determines an inspection condition for the object based on the inputted wavelength characteristic of the object and the learning; and
a defect detector which detects a defect of the object based on the determined inspection condition of the object.
According to a twelfth aspect of the invention, the defect inspection method of the eleventh aspect, the learning unit learns and stores a relationship between the inputted wavelength characteristic of the object and the determined inspection condition for the object as the learning.
According to a thirteenth aspect of the invention, the defect inspection method of the eleventh aspect, the learning unit learns and stores the relationship between the wavelength characteristic of the following sample and the inspection condition for the following sample referring to the relationship between the wavelength characteristic of the previous sample and the inspection condition for the previous sample.
According to a fourteenth aspect of the invention, the defect inspection method of the eleventh aspect, the learning unit includes a neural network.
According to a fifteenth aspect of the invention, the defect inspection method of the eleventh aspect, the defect inspection apparatus includes an apparatus for macroscopically inspecting the presence of the defect on the object having a minute pattern.
According to a sixteenth aspect of the invention, the defect inspection method of the eleventh aspect further includes:
an illuminating unit which illuminates each of the samples and the object while varying a wavelength of the illumination light;
an image unit which captures images of each of the samples and the object using reflected lights or transmitted lights of the illumination lights having different wavelengths; and
a wavelength characteristic detector which detects the wavelength characteristic of each of the samples and the object based on the images captured using the illumination lights having different wavelengths.
According to a seventeenth aspect of the invention, the defect inspection method of the sixteenth aspect, the wavelength characteristic includes luminance information of the reflected lights or the transmitted lights with the wavelength variation of the illumination light.
According to an eighteenth aspect of the invention, the defect inspection method of the eleventh aspect, the defect detector executes differential processing between a reference image data and an image of the object for obtaining a differential image, binarizes the differential image for obtaining a binarization data, and detects the defect based on the binarization data.
According to a nineteenth aspect of the invention, the defect inspection method of the eighteenth aspect, the inspection condition includes a threshold level referred for binarizing.
According to a twelfth aspect of the invention, the defect inspection method of the eighteenth aspect, the defect detector filters the differential image, the inspection condition includes a parameter for filtering.